TY - JOUR
T1 - Application of tensor factorisation for CAE model preparation form CAD assembly models
AU - Boussuge, Flavien
AU - Armstrong, Cecil G.
AU - Tierney, Christopher M.
AU - Robinson, Trevor T.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - Generating fit-for-purpose CAE models from complex CAD assemblies is time consuming and error-prone. Tedious tasks include identifying and isolating the components of interest, removing duplicate components, and correcting inconsistent component interfaces. In this paper a new approach to help engineers identify similar features and analyse the consistency of CAD assembly models is proposed. The method utilises a tensor factorisation technique developed for relational machine learning and applies it to B-Rep topological and geometrical relations. The model considers globally all the input relations to identify which entities in the assembly are similar (within a user-defined threshold) to a selected input entity. It is shown that a hierarchical clustering method can group entities, based on the similarities of their attributes and relationships with adjacent components. It is shown how some unsuspected CAD modelling errors show up as features which should be similar, but which are not. It is demonstrated how the technique can be used to support the, currently highly manual, task of decomposing a volume representing an internal fluid network into sub-volumes and features of significance.
AB - Generating fit-for-purpose CAE models from complex CAD assemblies is time consuming and error-prone. Tedious tasks include identifying and isolating the components of interest, removing duplicate components, and correcting inconsistent component interfaces. In this paper a new approach to help engineers identify similar features and analyse the consistency of CAD assembly models is proposed. The method utilises a tensor factorisation technique developed for relational machine learning and applies it to B-Rep topological and geometrical relations. The model considers globally all the input relations to identify which entities in the assembly are similar (within a user-defined threshold) to a selected input entity. It is shown that a hierarchical clustering method can group entities, based on the similarities of their attributes and relationships with adjacent components. It is shown how some unsuspected CAD modelling errors show up as features which should be similar, but which are not. It is demonstrated how the technique can be used to support the, currently highly manual, task of decomposing a volume representing an internal fluid network into sub-volumes and features of significance.
U2 - 10.1016/j.cad.2022.103372
DO - 10.1016/j.cad.2022.103372
M3 - Article
VL - 152
JO - Computer-Aided Design
JF - Computer-Aided Design
SN - 0010-4485
M1 - 103372
ER -